A Deep Learning approach for fire object detection in Autonomous vehicles

Abstract

In recent years, object detection has emerged as a crucial component of many popular consumer applications, including video surveillance and security systems, mobile text recognition, etc. The potential for autonomous vehicles (AVs) to increase driver satisfaction and decrease fatalities and injuries in traffic accidents has attracted a lot of interest in recent years. Object detection is critical to autonomous driving infrastructure. Autonomous automobiles need precise environmental interpretation to drive safely. Locating and identifying these things in real time is a significant challenge, but deep learning-based object detectors play a crucial role in this endeavour. In this paper, a prototype of the autonomous vehicle controlled by a microcontroller for fire object detection is proposed. Localization of the fire object in a picture using the deep learning model is performed using the live video feed from the camera installed on the prototype remote-operated car. The gadget may also pick up on specifics and alert the user. Experimental findings showed that the suggested prototype with deep learning architecture recognised and alerted devastating fires with high speed and accuracy in diverse weather conditions - sunny or overcast, day or night.